Quantum Journal

'How to handle errors' rather than the number of qubits… Mouser releases latest issue of Quantum Computing EIT - 벤처스퀘어

Qkabrine: A Joint Architecture, Encoding, and Hyperparameter Search Framework for Quantum Machine Learning

Building a quantum machine learning (QML) model competitive with a classical baseline currently requires a practitioner to separately choose a circuit architecture, a data-encoding scheme, a model paradigm (kernel versus variational), and a set of training hyperparameters, then verify after the fact that the chosen circuit is even trainable. Existing QML libraries provide the primitives for this but not the search, and existing classical AutoML...

How Quantum Is the Advantage? A Fair, Calibration- and Noise-Aware Benchmark and Attribution Audit of Quantum Machine Learning for Network Intrusion Detection

Quantum machine learning (QML) for network intrusion detection (NIDS) is routinely reported to reach near-perfect accuracy, yet the most rigorous studies find that well-tuned classical models remain competitive, and that apparent quantum gains may be artefacts of classical dimensionality reduction and implicit regularisation rather than genuine quantum effects. We ask not whether a quantum model can post a high accuracy, but how quantum the adva...

Observable Scaling Hierarchies in Multiphoton Dissipative Quantum Sensing

We investigate how quantum correlations in squeezed driving fields determine scaling laws in dissipative multiphoton quantum sensing. Independently squeezed fields yield \emph{factorized} scaling, with separate absorption and emission contributions and nonlinear thresholds that suppress exponential scaling in linear processes. In contrast, jointly squeezed fields generate \emph{collective} scaling governed by the total nonlinear photon order of...